Method and apparatus for surface inspection

ABSTRACT

Detection of a defect (18) on the surface (15) of an article (10), such as a semiconductor chip, is accomplished by illuminating the chip in a bright field and then capturing the image thereof with a television camera (30) coupled to a machine vision processor (32). To detect the defect 18, the vision processor first adaptively thresholds the captured image to effectively eliminate areas in the image brighter than those associated with the defect (18) which are usually dark. Thereafter, the vision processor (32) erodes and then dilates the dark areas within the image remaining after binarization to isolate those dark areas associated with the defect. The existence of a defect can then be established by the existence of a now-isolated dark area.

TECHNICAL FIELD

This invention relates to a method and apparatus for inspecting asurface on a substrate to detect defects, if any, thereon.

BACKGROUND OF THE INVENTION

There are many articles, such as semiconductor chips for example, which,during manufacture, become damaged and are either immediately renderedinoperative or have their operating lifetime reduced when their exposedsurface(s) becomes scratched or stained. Thus, during the manufacture ofsuch articles, one or more visual inspections are commonly performed todetect any surface defects, so that articles bearing a defect can berepaired, if possible, or if not, they can be scrapped prior to anysubsequent processing. Detection of defects at an early stage ofmanufacture helps to reduce manufacturing costs and improvemanufacturing yields.

Visual detection of surface defects is a relatively easy task when thearticle has relatively large surface features. Detection of defectsbecomes more difficult when the surface features are small, as in thecase of a semiconductor chip having an exposed surface whose featureshave a linewidth often no larger than several microns. The smalllinewidth of the features on the exposed surface of the semiconductorchip has heretofore made it impractical to employ present day automatedvision equipment to detect defects on the exposed chip surface. This isbecause most automated vision equipment accomplishes inspection by thetechnique of "pattern matching," which involves matching the image ofthe pattern of the article undergoing inspection to that of a perfect or"golden" pattern.

The process of detecting defects by the technique of pattern matchingrequires that the image of the pattern on the article undergoinginspection be accurately registered with the golden pattern. Otherwise,a match between the pattern on the article and the golden patternbecomes practically impossible to obtain. Registration of the pattern ofthe exposed surface on each of a plurality of chips, formed on a wafer,with a golden pattern, representing a set of perfectly formed chipfeatures, is possible because one or more fiducials are usually presenton the wafer. However, once the wafer is diced to separate the chips, itis difficult to register the pattern on each chip with the goldenpattern because of the extremely small size of the features and theabsence of any fiducials on the chip.

There is a need for a technique for visually detecting very smallsurface defects, such as scratches and stains, on a semiconductor chip,because often, such defects do not manifest themselves during electricaltesting of the chip. As a result, when a semiconductor chip bearing astain or crack is packaged to form an integrated circuit, there is thelikelihood that the integrated circuit may prematurely fail in the fieldas a result of vibration and thermal cycling.

SUMMARY OF THE INVENTION

Briefly, in accordance with a preferred embodiment of the invention, atechnique is provided for automated inspection of at least one surfaceof an article, such as a semiconductor chip, to detect defects, such asstains and scratches, which, when illuminated, tend to appear dark.Initially, the chip is illuminated by directing light at the surface ofthe chip so that the light strikes the surface substantially normal tothe plane thereof in order to maximize any scattering of the light bythe defects, if any, on the surface. Thereafter, the image of thesurface of the chip is captured by an image-acquisition device whoseoptical axis is substantially normal to the plane of the surface of thechip. The captured image is then adaptively binarized, typically, byassigning a first intensity value (usually zero) to those areas withinthe image whose actual intensity value is less than a particularthreshold value, which in practice, is set in accordance with ahistogram of the image intensity. Those areas in the image whoseintensity value is above the threshold are assigned a second intensityvalue, typically much greater than zero. As a result of binarization,the image of the surface now contains only dark (i.e., black) and verybright (i.e., white) areas.

After the image of the surface of the chip has been binarized, the imageis further processed to isolate each dark area which is associated witha non-reflective defect (if any), such as a scratch or stain, present onthe surface of the chip. In a preferred embodiment, the image isprocessed, following binarization, by repeatedly eroding all of the darkareas within the image of the chip until only the dark area associatedwith each defect remains. Thereafter, the remaining dark areas, eachassociated with a defect, are then repeatedly dilated the same number oftimes the dark areas were previously eroded. The number of erosions anddilations is dependent on the size (linewidth) of those dark areasassociated with features normally present on the surface of the chip.The presence of a dark area remaining in the image of the chip after theerosion and dilation operations signifies the existence of a defect onthe surface of the chip.

As may now be appreciated, the present defect detection method operateswithout resort to any pattern matching.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a block diagram of an apparatus, in accordance with apreferred embodiment of the present invention, for inspecting asemiconductor chip to detect defects, if any, on its exposed surface;

FIG. 2 is a flowchart representation of a program executed by theapparatus of FIG. 1 to inspect the semiconductor chip;

FIG. 3 shows the image of the chip captured by the apparatus of FIG. 1during execution of the program of FIG. 2;

FIG. 4 shows a histogram of the intensities of the pixels within theimage of FIG. 3;

FIG. 5 shows the image of the chip of FIG. 3 after transformation of thepixel intensities;

FIG. 6 is a histogram of the pixel intensities within the image of FIG.5 after the pixel intensities have been normalized;

FIG. 7 shows the image of the chip of FIG. 5 after binarization;

FIG. 8 shows the image of FIG. 7 after the dark areas therein have beeneroded and dilated to isolate each dark area associated with eachdefect; and

FIG. 9 is a flowchart representation of a program executed by theapparatus of FIG. 1 to detect defects on the semiconductor chip whichare both smaller and larger than the linewidth of the features on thechip.

DETAILED DESCRIPTION

FIG. 1 is a perspective view of a semiconductor chip 10, which issurrounded by, and which overlies a portion of, a volume of bondingmaterial 12 that secures the chip within a recess in a chip carrier 14such that the chip has its major surface 15 exposed. In the process ofbonding the chip 10 to the chip carrier 14, it is not unusual for theexposed surface 15, which has a plurality of very fine features 16thereon, to become scratched or stained, giving rise to one or moresurface defects 18, only one of which is shown. When the defect 18 is aslarge as or larger than the linewidth of the features 16 (which isusually on the order 1-5 microns), the defect may cause damage to thefeatures, possibly rendering the chip 10 defective by causing it to beinoperative or have a much reduced lifetime.

In FIG. 1 there is shown an apparatus 20, in accordance with a preferredembodiment of the invention, for detecting the defect 18 on the surface15 of the semiconductor chip 10. The apparatus 20 includes a lightsource 22, such as a tungsten-halogen lamp or the like, which produces abeam of light 24 that is directed into a beam splitter 26. The beamsplitter 26, which is conventional in its construction, directs the beam24 towards the chip 10 so that the beam strikes the surface 15 normal tothe plane thereof. In addition, the beam splitter 24 also serves todirect those portions of the beam 24, which are reflected normally fromthe surface 15, into a lens 28 mounted on an image-acquisition device30, typically taking the form of a television camera whose optical axisis substantially normal to the surface.

The technique of illuminating the chip 10 by directing the beam 24 atthe surface 15 normal to the plane of the surface and then sensing theintensity of the light reflected normal to the plane thereof is known as"bright field illumination." In contrast, the technique of directing thebeam 24 towards the surface 15 at an acute angle with respect to theplane thereof and then sensing the intensity of the light reflected fromthe surface normal to the plane thereof is referred to as "dark fieldillumination." Bright field illumination is preferable because the imageof the surface 15 captured by the television camera 30 will be brighterthan under dark field illumination.

Under bright field illumination, the intensity of the light expected tobe reflected into the lens 28 on the camera 30 from the area on thesurface 15 occupied by the defect 18 will equal a small portion of thetotal light scattered by the defect area. The area on the surface 15surrounding each defect 18 tends to reflect substantially all of thebeam 24 incident thereon into the lens 28. Therefore, the presence ofeach defect 18 can be detected by a large reduction in the lightreceived by the camera 30 from each area on the surface 15 occupied bythe associated defect.

Under dark field illumination, the intensity of the light expected to bereflected into the lens 28 from each area occupied by a defect 18 willalso equal a small portion of the total light scattered from theassociated defect area. However, under dark field illumination, the areasurrounding each defect 18 is not expected to reflect light into thelens 28. Therefore, the presence of a defect 18 will not cause asignificant change in the intensity of the light reflected into the lens28 on the camera 30 from each defect area, as compared to those areas onthe surface 15 not occupied by a defect. Hence, bright fieldillumination provides a more efficient technique for detecting eachdefect 18, such as a scratch or stain, on the surface 15.

The inspection apparatus 20 further includes a machine vision processor32 coupled to the television camera 30 for processing its output signal,typically an analog voltage that varies in accordance with the intensityof the image captured by the camera. In practice, the vision processor32 takes the form of a commercial vision system, such as the model P256vision system manufactured by IRI Corp., Carlsbad, California. Thevision processor 32 is coupled to a monitor 34 so that the output of thevision processor can be displayed.

The operation of the apparatus 20 may best be understood by reference toFIG. 2, which shows a flowchart representation of a computer programexecuted by the vision processor 32 to detect each defect 18 (if any) onthe surface 15 (all of FIG. 1). Referring to FIG. 2, upon execution ofthe program, the vision processor 32 of FIG. 1 initially executes step36 of FIG. 2 and obtains the image of the surface 15 of the chip 10captured by the camera 30, both of FIG. 1. To obtain the image of thesurface 15, the vision processor 32 converts the analog output signal ofthe camera 30 into a stream of digital signals, each representing theintensity of a separate small area (pixel) within the image. The digitalsignal representing the intensity of each pixel is typically eight bitsin length, so the intensity (as measured in shades of gray) of eachpixel ranges in value between 0 (black) and 255 (white).

Referring to FIG. 1, the vision processor 32 typically converts theoutput signal of the television camera 30 into 61,440 digital signals,each representing the intensity of a separate one of the pixels within a240×256 pixel array comprising the image of the surface 15 captured bythe camera. In practice, the optical properties of the lens 28 on thecamera 30 are selected such that each of the pixels within the 240×256array corresponds to a 5 μm×5 μm area on the surface 15. It should beunderstood that the particular size of the pixel array is dependent onthe characteristics of the vision processor 32 and that larger arraysare possible. Further, the physical size of each pixel within the arrayis dependent on the optical properties of the lens 28.

Referring now to FIG. 3, there is shown the image of the surface 15 ofthe chip 10 of FIG. 1 established by the vision processor 32 of FIG. 1during step 36 of FIG. 2. As can be observed in FIG. 3, the defect 18 isrepresented by a dark spot appearing near the upper left-hand corner ofthe image. Detection of the defect 18 would be a relatively easy task ifthere were no other dark areas within the image of FIG. 3. However, onthe surface 15 of FIG. 1 there are regularly occurring features 16 ofvarying gray levels, including individual cell walls, known as "tubs,"which appear in FIG. 3 as black squares and rectangles.

In order to detect the defect 18, it is necessary to isolate thoseregularly occurring features 16 from each area associated with a defect18. To facilitate isolation of the defect 18, it is useful to binarizeor threshold the image of FIG. 3 so that those features 16 which arebrighter (i.e., they have a higher pixel intensity or gray level) thanthe defect are made to appear white. As will be described below, theimage of FIG. 3 is binarized by setting to zero the intensity (graylevel) of those pixels in the image whose true intensity is below athreshold value t so the pixels now appear black. Those pixels withinthe image of the surface 15 whose true intensity is higher than thethreshold value t are assigned an intensity value of 255 so the pixelsnow appear white.

Before the image shown in FIG. 3 can be binarized, the threshold value tmust be set. Referring to FIG. 2, following step 36, the visionprocessor 32 of FIG. 1 adaptively sets the threshold value t during step38. In practice, the vision processor 32 adaptively sets the thresholdvalue t during step 38 of FIG. 2 in accordance with the pixel intensityfrequency, that is, the number of pixels within the image of FIG. 3 thathave a particular intensity level. A plot of the pixel intensityfrequency, often referred to as a histogram, is shown in FIG. 4.

There are several techniques by which the vision processor 32 of FIG. 1can adaptively set the threshold value t in accordance with the pixelintensity frequency. For example, the threshold value t can be set equalto the mean of the intensity of the pixels in the image of FIG. 3 lessthe product of k₁ and the pixel intensity variance, where k₁ is aconstant. The pixel intensity mean and variance are established by thevision processor 32 of FIG. 1 from the pixel histogram shown in FIG. 4.Another approach is to set the threshold value t equal to the product ofk₂ and the mean of the intensity of the pixels, where k₂ is a constantless than unity. Both of these approaches, which depend on the mean ofthe intensity of the pixels within the image of FIG. 3, have been foundto be dependent of the type of semiconductor chip 10 undergoinginspection.

Yet another approach to establishing the threshold value t is to set thevalue equal the product of k₃, where k₃ is a constant less than unity,and the pixel intensity at a point just below the first non-zero peak orknee of the histogram of FIG. 4 where the left-side slope is greaterthan the right-side slope. The pixel intensity at the point just belowthe first non-zero histogram peak has been found to be just above theintensity of those pixels within the image of FIG. 3 corresponding tothe defect 18. Establishing the threshold value t in the manner justdescribed has been found to be independent of the type of chip 10undergoing inspection and is the preferable approach. However, it shouldbe noted that this approach tends to be somewhat more sensitive tovariations in the illumination of the surface 15 of the chip 10 of FIG.1 as compared to the other two approaches which rely on the mean of thepixel intensity.

When establishing the threshold value t in accordance with the pixelintensity at a point just below the first non-zero peak of the histogramof FIG. 4, it is useful to expand or stretch the histogram in order tomake the first non-zero peak more prominent. This may be accomplished byfirst assigning an intensity value, equal to the mean pixel intensity,to those pixels within the image of FIG. 3 whose intensity exceeds themean intensity. FIG. 5 shows the image of FIG. 3 after theabove-described pixel intensity transformation has been made.Thereafter, the intensity of each of the pixels in the image of FIG. 5is normalized (i.e., scaled) so the range of the pixel intensities isnow 0-255. FIG. 6 shows the histogram of the image of FIG. 5 after theintensity of the pixels in the image has been normalized, and after thehighest peak associated with the pixels which now have an intensityvalue equal to the mean intensity, has been discarded. As may now beappreciated by comparison of FIGS. 4 and 6, the first non-zero peak inthe histogram of FIG. 6 is more prominent, allowing the threshold valuet to be more easily established.

Once the threshold intensity value t has been established during step 38of FIG. 2, the image corresponding to the histogram of FIG. 6 (i.e., thenormalized image of FIG. 5) is then binarized during step 40 of FIG. 2.Binarization of the image is accomplished during step 40 of FIG. 2 bysetting those pixels within the image, whose true intensity is below thevalue t, to a zero gray level so the pixel appears black, whereas thosepixels whose true intensity is above the value t are accorded a graylevel of 255 so the pixels appear white. FIG. 7 shows the binarizedimage of the surface of the chip 10 of FIG. 1 following step 40 of FIG.2. As can be seen in FIG. 7, the only regions within the binarized imageof the chip 10 which appear dark are the defect 18 and the features 16(i.e., the tubs) which normally appear dark.

Following step 40 of FIG. 2, the image of FIG. 7 is further processedduring step 42 to remove therefrom the features 16 (i.e., the tubs)which regularly appear dark in the image in order to isolate the darkarea in FIG. 7 associated with the defect 18. Removal of the regularlyoccurring dark features 16 within the image of FIG. 7 can beaccomplished during step 42 of FIG. 2 in several different ways. Thesimplest and most efficient method is to first repetitively erode(diminish) and then repetitively dilate (expand) all of the dark areas(i.e., the areas associated with the dark, regularly occurring features16 and the defect 18) within the image of FIG. 7. Erosion and dilationof dark areas within the image of FIG. 7 is known in the art as amorphological "opening" operation.

Erosion of the dark areas within the image of FIG. 7 is accomplished bycausing those dark (i.e., black) pixels, within a ring one or two pixelswide, contiguous with the periphery of each of the dark features 16 andthe defect 18, to now appear white. As a result, a portion of each darkfeature and a portion of the defect 18 within the image of FIG. 7 areremoved (i.e., shrunk), causing the features and the defect to appearsmaller. The dark areas in the image of FIG. 7 are repeatedly dilateduntil those dark areas associated with the regularly occurring feature16 in FIG. 3 are eliminated. The number of erosions is determined apriori by the linewidth of the features 16 in FIG. 3. Typically, onlyone or two dilation operations are necessary before all of the darkareas associated with the regularly occurring features 16 areeliminated, as seen in FIG. 8. All that remains in FIG. 8 is the darkarea associated with each defect 18.

After the dark areas within the image of FIG. 7 are repeatedly eroded,any dark areas which remain, as seen in FIG. 8, are then dilated thesame number of times as the areas were eroded. Each dark area remainingwithin the image of FIG. 8 is successively dilated by causing thosewhite pixels, within a ring one or two pixels wide circumscribing theperiphery of the dark area, to now appear dark (i.e., black). In thisway, the size of the defect 18 in FIG. 8, is increased after eachdilation. Note that any of the dark areas in the image of FIG. 7 whichhad been removed (e.g., turned white) after the erosion operation do notreappear after dilation.

The reason why successive erosion of the dark areas within the image ofFIG. 7 causes the regularly occurring dark features 16 to be removed isthat, in practice, the regularly occurring dark features (i.e., thetubs) tend to be much narrower than the defect 18. Since the darkfeatures 16, whose corresponding dark areas remain within the image ofFIG. 3 following binarization thereof during step 40 of FIG. 2, tend tobe much narrower than the defects 18, the dark areas corresponding tothe features tend to disappear after only a few erosions, whereas thedark areas associated with the defect do not. Note that the size of thedefect 18 in FIG. 8 is about the same as in FIG. 7. The reason why isthat the defect 18 is substantially returned to its former size by thesubsequent dilations performed after the erosion operations have beencompleted.

There are alternative approaches that may be employed during step 42 ofFIG. 2 to remove the regularly occurring dark features 16 present in theimage of FIG. 7. For example, during step 42 of FIG. 2, the intensity ofthe pixels within the image of FIG. 7 could be modified using a low passfilter (not shown) and then thresholded in order to remove those pixelsassociated with the dark, regularly occurring features 16. However, inpractice, the method of repetitively eroding and dilating the dark areasin the image of FIG. 7 in succession to isolate the dark area associatedwith the defect 18 was found to be more efficient.

Referring to FIG. 2, after step 42, the location and the size of thedefect 18 of FIG. 1 are established during step 44. The location andarea of the defect 18 are determined from the location and size of thedark area within the image of FIG. 8. As may be appreciated, since allof the regularly occurring dark features 16 have already been eliminatedfrom the image of FIG. 7 during step 42 of FIG. 2, the only dark areawhich remains within the image, as seen in FIG. 8, is associated withthe defect 18. The location and size of the dark area in FIG. 8 can bedetermined from the location and number of dark (i.e., black) pixelswithin the image. When multiple dark areas are present following step 42of FIG. 2, it becomes necessary to perform a connectivity analysis todetermine how the areas are connected in order to determine the size andlocation of the individual defects 18.

The foregoing describes a technique for inspecting a surface 15 on anarticle 10, such as a semiconductor chip, without the need to match thepattern on the surface of the chip to that of a golden pattern to detecta defect 18. The present method effectively detects a defect 18 which,although small, has at least a slightly larger linewidth than the dark,regularly occurring features 16 (i.e., the tubs) on the surface 15 ofthe chip 10.

In addition to isolating each defect 18 of FIG. 1 which is larger thanthe linewidth of the regularly occurring features 16 of FIG. 1, thetechnique described above can be employed to isolate defects which aresmaller than the linewidth of the features. In order to isolate thesmall defects 18, the level of illumination of the surface 15 within thefield of view of the camera 30 of FIG. 1 must be substantially uniformso that the linewidths of the regularly occurring features 16 fallwithin a predetermined narrow band. Otherwise, isolation of the smalldefects 18 may prove extremely difficult.

Isolation of each defect 18 smaller than the linewidth of the regularlyoccurring features 16 on the surface 15 of the chip 10 of FIG. 1 isaccomplished in much the same way that larger defects are isolated.First, the image of the surface 15 of the chip 10 is obtained, as perstep 36 of FIG. 2. The image obtained during step 36 is then binarized,as per step 38 of FIG. 2. Thereafter, the dark areas within thebinarized image are eroded and then dilated in the manner described withrespect to step 42 of FIG. 2, not to remove the regularly occurringfeatures 16, but to remove each small defect 18. The number of erosions,and the corresponding number of dilations, is determined a priori by theminimum linewidth of the features 16 in FIG. 3 such that each smalldefect 18 is eliminated while the regularly occurring features, andthose large defects, if any, remain.

From the image containing only the regularly occurring features 16 andthe large defects 18, the image obtained during step 38 of FIG. 2 isthen substracted. The substraction is carried out by individuallysubstracting the intensity of each pixel within the image obtainedduring step 38 from each corresponding pixel within the image containingonly the regularly occurring features 16 and the large defects 18. Theresultant image obtained after subtraction is then inverted by causingeach pixel which is dark to now appear white and vice versa. Thenow-inverted image contains only those defects 18 smaller than thelinewidth of the regularly occurring features 16.

The techniques described above can be combined in the manner shown inflowchart form in FIG. 9 to obtain a composite image of those defects 18which are both smaller and larger than the linewidth of the regularlyoccurring features 16. Referring to FIG. 9, first, the defects 18 largerthan the linewidth of the features 16 are isolated (step 46) byperforming the steps 36-42 of FIG. 2. Then, the image of the chipobtained during step 38 of FIG. 2 is also processed (step 48), asdescribed above, to isolate the small defects 18. The image containingonly the small defects 18 is logically ANDed (step 50) with the imagecontaining only the large defects. The two images are logically ANDed bycausing each pixel in the resultant image to appear white only if eachof the corresponding pixels in the images containing only the small andthe large defects 18 each appear white. For any other combination, thecorresponding pixel in the resultant image obtained by ANDing the imagescontaining only the large and the small defects 18 appears black.

Further, the regularly occurring features 16 can themselves be isolatedfrom those defects 18 which are either smaller or larger. To isolate theregularly occurring features 16, the image containing the regularlyoccurring features and the large defects 18 is subtracted from the imagecontaining only the larger defects. The resultant image obtained aftersubtraction is then inverted. The image which is obtained afterinversion now contains only the regularly occurring features 16.

The foregoing described a technique for inspecting a surface byisolating, from the regularly occurring features 16, those defects 18which are either smaller or larger than the linewidth of the features.

It is to be understood that the above-described embodiments are merelyillustrative of the principles of the invention. Various modificationsand changes may be made thereto by those skilled in the art which willembody the principles of the invention and fall within the spirit andscope thereof.

We claim:
 1. A method for inspecting a surface of an article, having atleast one feature thereon, to detect defects, if any, which are eithersmaller or larger than the linewidth of the feature, the methodcomprising the steps of:illuminating the surface of the article withlight which is directed to strike the surface substantially normal tothe plane thereof so that upon illumination of the surface, each defectappears dark; capturing the image of the surface of the article with animage-acquisition device whose optical axis is substantially normal tothe axis of the surface; binarizing the captured image to cause thoseareas within the image having an intensity below a threshold value toappear dark, and those areas having an intensity above the thresholdvalue to appear bright; processing the binarized image to produce afirst image in which those defects, if any, which are larger than thelinewidth of the feature, are isolated; processing the binarized imageto produce a second image in which those defects, if any, which aresmaller than the linewidth of the feature, are isolated; logicallyANDing the first and second images to yield a third image; andestablishing the presence of a defect by the existence in the thirdimage of a dark area.
 2. A method for processing an image containingboth regularly occurring features and at least one defect which may besmaller or larger than the linewidth of the feature, to isolate thefeature from the defect, the method comprising the steps of:binarizingthe image to cause those areas within the image having an intensitybelow a threshold value to appear dark, and those areas having anintensity above the threshold value to appear bright; processing thebinarized image by eroding and dilating each dark area therein to yielda first image containing only the defects, if any, larger than thefeature; processing the binarized image by eroding and dilating eachdark area to yield a second image in which each defect, if any, which issmaller than the linewidth of the feature, is effectively eliminated,while the feature, and the defects, if any, larger than the featureremain; and subtracting the second image from the first image and theninverting the resultant image, the inverted resultant image nowcontaining only the regularly occurring feature.
 3. The apparatusaccording to claim 1 wherein the image-acquisition means comprises atelevision camera.
 4. The apparatus according to claim 2 wherein themeans for binarizing the image, the means for processing the image, andthe means for establishing the size of the defects comprise a machinevision processor coupled to the television camera.